Performance analysis of sentiments in Twitter dataset using SVM models

Ramasamy, Lakshmana Kumar; Kadry, Seifedine; Nam, Yunyoung; Meqdad, Maytham N.

doi:10.11591/ijece.v11i3.pp2275-2284

Cited by 30 publications

(22 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ramanathan et al, established a theoretical model to enable retailers to better serve their customers using insights from social sites data [25]. Ramasamy et al, employed support vector machines to classify Twitter data into positive, negative and neutral sentiments to improve decision making process for business [26]. To address early risk assessment challenges, a supervised text classification approach was introduced by Burdisso et al, in 2019 [27].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Mehmood¹,

Ahmad²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Education sector has witnessed several changes in the recent past. These changes have forced private universities into fierce competition with each other to get more students enrolled. This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands. To get competitive gain, universities must observe and examine the students' feedback on their own social media sites along with the social media sites of their competitors. This study presents a novel framework which integrates numerous analytical approaches including statistical analysis, sentiment analysis, and text mining to accomplish a competitive analysis of social media sites of the universities. These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received, major areas for improvement. A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan. Experimental results show that diversity of shared content, frequency of posts, and schedule of updates, are the key areas for improvement for the local universities. Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university (RIU) Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

show abstract

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Mehmood¹,

Ahmad²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) is one of the most popular supervised learning algorithms that finds the optimal hyperplane, which separates the data points into two-component by maximizing the margin, which represents the distance from the decision surface to the closest data point [23], [24]. SVM is effective in cases where the number of dimensions is greater than the number of samples given.…”

Section: Support Vector Machine Algorithmmentioning

confidence: 99%

Fake accounts detection on social media using stack ensemble system

Ali

Abdullah

2022

IJECE

View full text Add to dashboard Cite

<p>In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.</p>

show abstract

“…) is the dot product of one of the support vectors with the test sample , and are the learning method that will determine numerical parameters such as weights [24]. Furthermore, the algorithm steps for SVM firstly define the best hyperplane.…”

Section: Support Vector Machine (Svm) Approachmentioning

confidence: 99%

A Comparative Study for Supervised Learning Algorithms to Analyze Sentiment Tweets

Fadel

Behadili

2022

eijs

View full text Add to dashboard Cite

Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness of three commonly used supervised learning classifiers, Naive Bayes, Logistic Regression, and Support Vector Machine. This is achieved by using two method of feature selection involving count Vectorizer and Term-Frequency-Inverse Data Frequency. The findings showed that the support vector machine classifier had achieved the highest accuracy of 91%, by feature selection: Count Vectorizer. But it is time consuming. For both accuracy and execution time concentrates, logistic regression is recommended.

show abstract

Performance analysis of sentiments in Twitter dataset using SVM models

Cited by 30 publications

References 14 publications

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Fake accounts detection on social media using stack ensemble system

A Comparative Study for Supervised Learning Algorithms to Analyze Sentiment Tweets

Contact Info

Product

Resources

About